Importing Necessary Libraries¶

In [49]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import joblib
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

Retriving and preprocessing the data¶

In [3]:
data = pd.read_csv("tumor_data.csv")
data.head()
Out[3]:
id diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean ... texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst Unnamed: 32
0 842302 M 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 ... 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890 NaN
1 842517 M 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 ... 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902 NaN
2 84300903 M 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 ... 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758 NaN
3 84348301 M 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 ... 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300 NaN
4 84358402 M 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 ... 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678 NaN

5 rows × 33 columns

In [4]:
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 33 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   id                       569 non-null    int64  
 1   diagnosis                569 non-null    object 
 2   radius_mean              569 non-null    float64
 3   texture_mean             569 non-null    float64
 4   perimeter_mean           569 non-null    float64
 5   area_mean                569 non-null    float64
 6   smoothness_mean          569 non-null    float64
 7   compactness_mean         569 non-null    float64
 8   concavity_mean           569 non-null    float64
 9   concave points_mean      569 non-null    float64
 10  symmetry_mean            569 non-null    float64
 11  fractal_dimension_mean   569 non-null    float64
 12  radius_se                569 non-null    float64
 13  texture_se               569 non-null    float64
 14  perimeter_se             569 non-null    float64
 15  area_se                  569 non-null    float64
 16  smoothness_se            569 non-null    float64
 17  compactness_se           569 non-null    float64
 18  concavity_se             569 non-null    float64
 19  concave points_se        569 non-null    float64
 20  symmetry_se              569 non-null    float64
 21  fractal_dimension_se     569 non-null    float64
 22  radius_worst             569 non-null    float64
 23  texture_worst            569 non-null    float64
 24  perimeter_worst          569 non-null    float64
 25  area_worst               569 non-null    float64
 26  smoothness_worst         569 non-null    float64
 27  compactness_worst        569 non-null    float64
 28  concavity_worst          569 non-null    float64
 29  concave points_worst     569 non-null    float64
 30  symmetry_worst           569 non-null    float64
 31  fractal_dimension_worst  569 non-null    float64
 32  Unnamed: 32              0 non-null      float64
dtypes: float64(31), int64(1), object(1)
memory usage: 146.8+ KB
In [5]:
# id and Unnamed: 32 is useless for our usecase
data = data.drop(["id", "Unnamed: 32"], axis=1)
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 31 columns):
 #   Column                   Non-Null Count  Dtype  
---  ------                   --------------  -----  
 0   diagnosis                569 non-null    object 
 1   radius_mean              569 non-null    float64
 2   texture_mean             569 non-null    float64
 3   perimeter_mean           569 non-null    float64
 4   area_mean                569 non-null    float64
 5   smoothness_mean          569 non-null    float64
 6   compactness_mean         569 non-null    float64
 7   concavity_mean           569 non-null    float64
 8   concave points_mean      569 non-null    float64
 9   symmetry_mean            569 non-null    float64
 10  fractal_dimension_mean   569 non-null    float64
 11  radius_se                569 non-null    float64
 12  texture_se               569 non-null    float64
 13  perimeter_se             569 non-null    float64
 14  area_se                  569 non-null    float64
 15  smoothness_se            569 non-null    float64
 16  compactness_se           569 non-null    float64
 17  concavity_se             569 non-null    float64
 18  concave points_se        569 non-null    float64
 19  symmetry_se              569 non-null    float64
 20  fractal_dimension_se     569 non-null    float64
 21  radius_worst             569 non-null    float64
 22  texture_worst            569 non-null    float64
 23  perimeter_worst          569 non-null    float64
 24  area_worst               569 non-null    float64
 25  smoothness_worst         569 non-null    float64
 26  compactness_worst        569 non-null    float64
 27  concavity_worst          569 non-null    float64
 28  concave points_worst     569 non-null    float64
 29  symmetry_worst           569 non-null    float64
 30  fractal_dimension_worst  569 non-null    float64
dtypes: float64(30), object(1)
memory usage: 137.9+ KB

Here diagnosis is out target data. It has M for malignant and B for benign. I want is as integer value.

In [11]:
pd.set_option('future.no_silent_downcasting', True) # suppress future_warning and enable the future behavior
data["diagnosis"] = data["diagnosis"].replace({"M": 1, "B": 0})
data["diagnosis"]
Out[11]:
0      1
1      1
2      1
3      1
4      1
      ..
564    1
565    1
566    1
567    1
568    0
Name: diagnosis, Length: 569, dtype: int64

Visualize the data and it's relation¶

In [12]:
data.head()
Out[12]:
diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean ... radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 1 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 ... 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 ... 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 1 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 ... 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 1 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 ... 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 1 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 ... 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

5 rows × 31 columns

In [20]:
data.describe()
Out[20]:
diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean ... radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
count 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 ... 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000 569.000000
mean 0.372583 14.127292 19.289649 91.969033 654.889104 0.096360 0.104341 0.088799 0.048919 0.181162 ... 16.269190 25.677223 107.261213 880.583128 0.132369 0.254265 0.272188 0.114606 0.290076 0.083946
std 0.483918 3.524049 4.301036 24.298981 351.914129 0.014064 0.052813 0.079720 0.038803 0.027414 ... 4.833242 6.146258 33.602542 569.356993 0.022832 0.157336 0.208624 0.065732 0.061867 0.018061
min 0.000000 6.981000 9.710000 43.790000 143.500000 0.052630 0.019380 0.000000 0.000000 0.106000 ... 7.930000 12.020000 50.410000 185.200000 0.071170 0.027290 0.000000 0.000000 0.156500 0.055040
25% 0.000000 11.700000 16.170000 75.170000 420.300000 0.086370 0.064920 0.029560 0.020310 0.161900 ... 13.010000 21.080000 84.110000 515.300000 0.116600 0.147200 0.114500 0.064930 0.250400 0.071460
50% 0.000000 13.370000 18.840000 86.240000 551.100000 0.095870 0.092630 0.061540 0.033500 0.179200 ... 14.970000 25.410000 97.660000 686.500000 0.131300 0.211900 0.226700 0.099930 0.282200 0.080040
75% 1.000000 15.780000 21.800000 104.100000 782.700000 0.105300 0.130400 0.130700 0.074000 0.195700 ... 18.790000 29.720000 125.400000 1084.000000 0.146000 0.339100 0.382900 0.161400 0.317900 0.092080
max 1.000000 28.110000 39.280000 188.500000 2501.000000 0.163400 0.345400 0.426800 0.201200 0.304000 ... 36.040000 49.540000 251.200000 4254.000000 0.222600 1.058000 1.252000 0.291000 0.663800 0.207500

8 rows × 31 columns

In [21]:
sns.pairplot(data, hue='diagnosis')
Out[21]:
<seaborn.axisgrid.PairGrid at 0x7fb71e9c3ca0>
No description has been provided for this image

This pairplot shows that every data of the feature is able to kind of distinguishing the Melignant and Benign tumor.¶

In [33]:
sns.scatterplot(data=data, 
                x='texture_mean', 
                y='perimeter_mean', 
                hue='diagnosis',            # Color by diagnosis
                style='diagnosis',          # Different markers by diagnosis
                markers={1: 'X', 0: 'o'})    # '1' = square (malignant), '0' = X (benign)

plt.title('Texture Mean vs Perimeter Mean')
plt.show()
No description has been provided for this image
In [34]:
count_diagnosis = data['diagnosis'].value_counts()

print(count_diagnosis)

diagnosis
0    357
1    212
Name: count, dtype: int64
In [36]:
# Group the data by 'diagnosis' and calculate the mean for each group
mean_values = data.groupby('diagnosis').mean()

mean_values
Out[36]:
radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean fractal_dimension_mean ... radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
diagnosis
0 12.146524 17.914762 78.075406 462.790196 0.092478 0.080085 0.046058 0.025717 0.174186 0.062867 ... 13.379801 23.515070 87.005938 558.899440 0.124959 0.182673 0.166238 0.074444 0.270246 0.079442
1 17.462830 21.604906 115.365377 978.376415 0.102898 0.145188 0.160775 0.087990 0.192909 0.062680 ... 21.134811 29.318208 141.370330 1422.286321 0.144845 0.374824 0.450606 0.182237 0.323468 0.091530

2 rows × 30 columns

Train the model¶

In [37]:
data.head()
Out[37]:
diagnosis radius_mean texture_mean perimeter_mean area_mean smoothness_mean compactness_mean concavity_mean concave points_mean symmetry_mean ... radius_worst texture_worst perimeter_worst area_worst smoothness_worst compactness_worst concavity_worst concave points_worst symmetry_worst fractal_dimension_worst
0 1 17.99 10.38 122.80 1001.0 0.11840 0.27760 0.3001 0.14710 0.2419 ... 25.38 17.33 184.60 2019.0 0.1622 0.6656 0.7119 0.2654 0.4601 0.11890
1 1 20.57 17.77 132.90 1326.0 0.08474 0.07864 0.0869 0.07017 0.1812 ... 24.99 23.41 158.80 1956.0 0.1238 0.1866 0.2416 0.1860 0.2750 0.08902
2 1 19.69 21.25 130.00 1203.0 0.10960 0.15990 0.1974 0.12790 0.2069 ... 23.57 25.53 152.50 1709.0 0.1444 0.4245 0.4504 0.2430 0.3613 0.08758
3 1 11.42 20.38 77.58 386.1 0.14250 0.28390 0.2414 0.10520 0.2597 ... 14.91 26.50 98.87 567.7 0.2098 0.8663 0.6869 0.2575 0.6638 0.17300
4 1 20.29 14.34 135.10 1297.0 0.10030 0.13280 0.1980 0.10430 0.1809 ... 22.54 16.67 152.20 1575.0 0.1374 0.2050 0.4000 0.1625 0.2364 0.07678

5 rows × 31 columns

In [41]:
# Separating the target data and features data
X = data.drop("diagnosis", axis = 1)
Y = data["diagnosis"]

# Splitting the data into training and tesing
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state = 42)

# Print the shape of the resulting datasets
print(f"Training data (X_train): {X_train.shape}")
print(f"Testing data (X_test): {X_test.shape}")
print(f"Training labels (Y_train): {Y_train.shape}")
print(f"Testing labels (Y_test): {Y_test.shape}")

Training data (X_train): (455, 30)
Testing data (X_test): (114, 30)
Training labels (Y_train): (455,)
Testing labels (Y_test): (114,)
In [44]:
# Training the model
# Initialize the Logistic Regression model
model = LogisticRegression(max_iter=4000, random_state=42)

# Train the model on the training data
model.fit(X_train, Y_train)
Out[44]:
LogisticRegression(max_iter=4000, random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(max_iter=4000, random_state=42)

Evaluate the model¶

In [46]:
# Accuracy on the training data
Y_training_predict = model.predict(X_train)
training_accuracy = accuracy_score(Y_train, Y_training_predict)
print(f"Accuracy on the training data: {(training_accuracy * 100):.2f}")

# Accuracy on the test data
Y_test_predict = model.predict(X_test)
test_accuracy = accuracy_score(Y_test, Y_test_predict)
print(f"Accuracy on the training data: {(test_accuracy * 100):.2f}")

Accuracy on the training data: 96.26
Accuracy on the training data: 95.61
In [48]:
# Confusion matrix on the test data
conf_matrix_test = confusion_matrix(Y_test, Y_test_predict)

print(conf_matrix_test)

# Create a heatmap to visualize the confusion matrix
plt.figure(figsize=(6, 5))
sns.heatmap(conf_matrix_test, annot=True, fmt='d', cmap='Blues', xticklabels=['Benign (0)', 'Malignant (1)'], yticklabels=['Benign (0)', 'Malignant (1)'])

# Set plot labels and title
plt.title('Confusion Matrix')
plt.xlabel('Predicted')
plt.ylabel('True')

# Show the plot
plt.show()

[[70  1]
 [ 4 39]]
No description has been provided for this image
In [50]:
joblib.dump(model, 'brease_cancer_prediction_model.pkl')  # Save the model
Out[50]:
['brease_cancer_prediction_model.pkl']

Test the model on some single data¶

In [56]:
# taking the data
input_data = (12.68,23.84,82.69,499,0.1122,0.1262,0.1128,0.06873,0.1905,0.0659,0.4255,1.178,2.927,36.46,0.007781,0.02648,0.02973,0.0129,0.01635,0.003601,17.09,33.47,111.8,888.3,0.1851,0.4061,0.4024,0.1716,0.3383,0.1031)
input_data_arr = np.array(input_data)
input_data_arr = input_data_arr.reshape(1, -1)
final_input_data = pd.DataFrame(input_data_arr, columns = X_train.columns)

prediction = model.predict(final_input_data)

# prediction probabilities
probabilities = model.predict_proba(final_input_data)
prob_cancerous = probabilities[0][1]
prob_non_cancerous = probabilities[0][0]

# Display the result along with the confidence
if prediction[0] == 0:
    print(f"This tumor is not cancerous with a confidence of {prob_non_cancerous*100:.2f}%")
else:
    print(f"This tumor is cancerous with a confidence of {prob_cancerous*100:.2f}%")

This tumor is cancerous with a confidence of 99.67%
In [ ]: